Computer vision development services for images and video
We build vision systems that inspect products, read documents, count and track objects and monitor sites, running in the cloud, on edge devices or inside mobile apps.
ZedBlu offers computer vision development services for businesses that want cameras and images to produce usable data. We build systems that detect defects on a production line, read text from documents and labels, count people, vehicles or stock, check safety gear on site and power camera features inside mobile apps.
Vision projects succeed or fail on data and deployment conditions, not on model choice alone. We start by studying your real images, lighting, camera angles and edge cases, then decide whether a pretrained model, a fine-tuned detector such as YOLO or a custom-trained network fits best, and whether it should run in the cloud, on an edge device or on the phone itself.
We also handle everything around the model: data collection and labeling, camera and hardware recommendations, dashboards and alerts, and integration with your ERP, quality or security systems. You own the trained models, labeled datasets and code.
What our Computer Vision Development team delivers
Visual inspection and defect detection
AI visual inspection for manufacturing, packaging and food processing: scratches, dents, missing parts, label errors, fill levels and color variation. We tune detection thresholds with your quality team, log every rejected item with an image for review, and connect results to your line controls or quality records so defects are traced to a batch or shift.
OCR and document vision
OCR software development for invoices, receipts, IDs, meter readings, number plates, shipping labels and handwritten forms, including multilingual documents. We pair text recognition with layout analysis and field validation, so the output is clean structured data rather than raw text that someone still has to retype.
Object detection and counting
We build custom object detection models that find, classify and count items in images or live video: vehicles at a gate, people in a store, boxes on a pallet, animals on a farm or products on a shelf. Tracking across frames avoids double counts, and results stream to dashboards or trigger alerts when counts cross a limit.
Video analytics for safety and security
Video analytics solutions that work with your existing IP cameras to detect PPE violations, intrusions into restricted zones, loitering, smoke or crowding. Events are clipped, time-stamped and sent to the right person by app, SMS or WhatsApp. We design for privacy, with options to blur faces and keep footage on your own servers.
Edge AI deployment
Our edge AI development puts models on devices such as NVIDIA Jetson, Raspberry Pi or industrial PCs close to the camera, which cuts bandwidth, keeps working when the internet drops and responds in real time. We optimize models with quantization and TensorRT or ONNX Runtime, and manage updates to the fleet over the air.
Vision features in mobile apps
Computer vision app development for iOS and Android: barcode and document scanning, product recognition, photo quality checks, measurements and on-device image recognition. On-device models in Core ML or TensorFlow Lite keep features fast and private, while heavier analysis can run in the cloud when the user has a connection.
What you get
Tested on your real conditions
We validate models on images from your own cameras, shifts and seasons, including glare, dust and motion blur, so accuracy figures reflect the site where the system will actually run.
Hardware advice without vendor bias
We recommend cameras, lenses, lighting and compute based on your use case and budget, and we can reuse existing CCTV where the image quality is good enough.
Privacy built into the design
Face blurring, on-premises processing, short retention periods and role-based access to footage support compliance with privacy laws in the regions where you operate, such as GDPR and India's DPDP Act.
Results people can act on
Detections become alerts, reports and records in the tools your team already uses, with image evidence attached, so supervisors can respond quickly and audit decisions later.
Our process
Tools & tech we use
Use cases
- Defect detection on manufacturing and packaging lines
- Number plate recognition for parking and gated sites
- PPE and safety compliance monitoring on construction sites
- Shelf and stock counting for retail stores and warehouses
- ID and document scanning in customer onboarding apps
- Crop, livestock and field monitoring from drone or camera images
Ready-made solutions to launch faster
Related services
Computer Vision Development: FAQ
How much do computer vision development services cost?
Cost depends on how many object or defect types the system must recognize, how much labeling is needed, the number of cameras and whether it runs on edge hardware or in the cloud. A software-only system with a web dashboard typically falls in the custom web app range of about $8,000 to $60,000+, with cameras and edge devices priced separately. A fixed quote follows our free consultation.
How many images do we need to train a vision model?
For a custom detector, a few hundred to a few thousand labeled images per class is a common starting point, but variety matters more than volume: different lighting, angles, backgrounds and defect types. Pretrained models reduce the need further. In image recognition software development, we usually begin with a small labeled set, measure results and then label more only where the model struggles.
How accurate will a computer vision system be?
Accuracy depends on image quality, how distinct the objects or defects are and how consistent the conditions are. Instead of promising a number upfront, we measure precision and recall on a test set from your site during the proof of concept, agree targets with you, and set thresholds that balance missed detections against false alarms for your process.
Can it work with our existing CCTV cameras?
Often, yes. Standard IP cameras that provide an RTSP stream at adequate resolution and frame rate work well for counting, intrusion and PPE detection. Fine inspection tasks, such as small scratches on a fast line, usually need industrial cameras and controlled lighting. We review sample footage before recommending any new hardware.
How long does a computer vision project take?
A proof of concept on sample images typically takes three to six weeks, including labeling. A production deployment with live cameras, edge devices, dashboards and integrations usually takes another six to twelve weeks. Hardware lead times, site access and how quickly labeled data can be prepared are the main factors that move the schedule.
Let's talk about
your project.
Tell us what you're building. A senior engineer — not a salesperson — replies within one business day with next steps.